Bidirectional Long Short-Term memory (Bi-LSTM) is a deep learning algorithm applied for forecasting the time series data. It is adopted to learn from the framework providing better understanding from the learning context[1]}. As Bi-LSTM is a multivariate time series it allows multiple time series dependent which can be designed together to predict the correlations along with the series recorded or captured variables varying simultaneously over time period[2]}. Bi-LSTM is a deep learning models for the sequential prediction without much error[3]}. It has many more features like handling temporal dependencies along with time series data distributing free learning models and flexibility in modelling non-linear features. In other words, Bi-LSTM is an enhanced version of LSTM algorithm in which it can deal with the combination of two variants having hidden states that allows information to come from the backward layer as well as from the forward layer. The Bi-LSTM is helpful for situation that require context input. It is widely used in classification especially like text classification, sentiment classification and speed classification and recognition and load forecasting. As Bi-LSTM is a deep learning models having capacity to capture non-linearity process and being flexible in modelling time-dependent data; so now-a-days Bi-LSTM have been using for real-time forecasts of the daily events[4]}.
